title: A graph kernel approach for alignment-free domain-peptide interaction prediction with an application to human SH3 domains.
abstract: BACKGROUND
  State-of-the-art experimental data for determining binding specificities of peptide recognition modules (PRMs) is obtained by high-throughput approaches like peptide arrays. Most prediction tools applicable to this kind of data are based on an initial multiple alignment of the peptide ligands. Building an initial alignment can be error-prone, especially in the case of the proline-rich peptides bound by the SH3 domains.
  
  
  RESULTS
  Here, we present a machine-learning approach based on an efficient graph-kernel technique to predict the specificity of a large set of 70 human SH3 domains, which are an important class of PRMs. The graph-kernel strategy allows us to (i) integrate several types of physico-chemical information for each amino acid, (ii) consider high-order correlations between these features and (iii) eliminate the need for an initial peptide alignment. We build specialized models for each human SH3 domain and achieve competitive predictive performance of 0.73 area under precision-recall curve, compared with 0.27 area under precision-recall curve for state-of-the-art methods based on position weight matrices. We show that better models can be obtained when we use information on the noninteracting peptides (negative examples), which is currently not used by the state-of-the art approaches based on position weight matrices. To this end, we analyze two strategies to identify subsets of high confidence negative data. The techniques introduced here are more general and hence can also be used for any other protein domains, which interact with short peptides (i.e. other PRMs).
  
  
  BACKGROUND
  The program with the predictive models can be found at http://www.bioinf.uni-freiburg.de/Software/SH3PepInt/SH3PepInt.tar.gz. We also provide a genome-wide prediction for all 70 human SH3 domains, which can be found under http://www.bioinf.uni-freiburg.de/Software/SH3PepInt/Genome-Wide-Predictions.tar.gz.
  
  
  BACKGROUND
  Supplementary data are available at Bioinformatics online.
authors.affiliation: Bioinformatics Group, Department of Computer Science, Georges-Köhler-Allee 106, 79110 Freiburg, Germany.
authors.forename: Kousik
authors.initials: K
authors.lastname: Kundu
authors.forename: Fabrizio
authors.initials: F
authors.lastname: Costa
authors.forename: Rolf
authors.initials: R
authors.lastname: Backofen
mesh_terms: D001185:Artificial Intelligence; D015894:Genome, Human; D006801:Humans; D008024:Ligands; D008958:Models, Molecular; D010455:Peptides / Q000737:chemistry* / Q000378:metabolism; D020539:Sequence Analysis, Protein / Q000379:methods*; D012984:Software; D018909:src Homology Domains
chemical_list: D008024:Ligands; D010455:Peptides